The market seeks, pays well and cannot find people.

A rule that rejects invalid CNPJ in supplier master data, with a compliance dashboard by area.

Grand View Research, 2023 to 2030. Fortune Business Insights projects US$ 14.53 bn and MarketsandMarkets, more conservative, US$ 10.54 bn.

BLR DATA, Data Management Maturity Index, based on 30 assessments since 2020

Gartner, Rita Sallam, July 2024. In February 2025 Gartner noted that 63% of organizations do not have data management practices suited for AI, or do not know whether they do.

State of Data Brazil 2024-2025, Data Hackers and Bain & Company, 5,217 respondents, collected between October and December 2024

Headline

About section

Outreach message

Quality bullet

Master data bullet

LGPD bullet

A recurring question

Conflict question

A question you should ask

• Basic concepts of data governanceWhat data governance is and what role it plays in a master data management program.
• Objectives and benefitsHow governance solves concrete organizational problems and sustains efficient information management.
• Governance structuresHow the governance structure is designed and who the actors involved in the process are.
• Roles and responsibilitiesThe responsibilities of each actor (data owner, data steward, custodian) and how each role contributes to the outcome.
• Policy developmentHow to define the policies that govern the governance structure, aligned with the organization's objectives.
• Data standardsWhy standardize and how the standard ensures consistency and quality across the entire information chain.
• Data catalogHow to document and organize the organization's data assets efficiently and make them searchable.
• Security, regulation, and privacyInformation security principles and protection of sensitive data, ensuring integrity and confidentiality.

• WelcomeIntroduction by Paulo Cordeiro: a straight talk about data, careers and what to expect from the program.
• About the courseHow the program is organized, what you will master by the end and how to make the most of the tracks, webinars and labs.
• Master data definitionWhat master data is, its importance in operations and in business strategies, and how the concept has evolved over time.
• Master Data ConceptsThe “single source of truth”: why consolidate information, what benefits it brings, and how MDM acts as an orchestrator between systems.
• Market size and opportunitiesThe impact of MDM on the advance of AI, ESG and digital transformation, the growth of the global market and the demand for qualified professionals.
• MDM trendsMulti-domain solutions, cloud, low-code, artificial intelligence and blockchain, and how these innovations open room for specialists.
• Career opportunitiesThe possible careers: master data analyst, data steward, implementation consultant, and the development path in each one.

• Introduction and concept of the master data hubThe role of the hub in defining, monitoring, and standardizing essential data.
• Master data lifecycleFrom creation and validation to archiving, with monitoring and data cleansing at every stage.
• Executive and operational meetingsHow to align strategic objectives, define responsibilities, and map processes and SLAs.
• Macroflow mappingIdentify gaps and inefficiencies and design the future process with stakeholders.
• KPIs and metricsTurnaround, accuracy, and rework: the indicators that show whether the hub works.
• Validations and feasibility analysisHow to ensure the proposed change is technically and financially sustainable.
• Approval and implementationCommittee presentation, strategic approval, team training and supported go-live.

• Origin of the service desk and the call centerThe evolution of customer service to today's tools and the effect of AI on support.
• What a service desk is forIncident logging and triage, specialized software and operational efficiency gains.
• Support channelsPhone, email, chat and self-service, and how to integrate them with the ticketing tool.
• How to size the teamTicket volume, average handling time, SLAs and channels.
• Types of ticketsIncident, request, problem, change and inquiry: how to classify and why.
• Classification and prioritizationThe GUT matrix (severity, urgency, trend) applied to the ticket queue.
• Support and logging toolsSelection criteria, market examples and the value of the knowledge base.
• Support indicatorsMTTR, AHT, first-contact resolution and volume: how to calculate and interpret them.
• Satisfaction surveyHow to measure satisfaction, design the survey and use the results to improve operations.

• Definition of data qualityWhy quality matters, how it affects operations, and which practices and technologies support reliable data.
• Data quality dimensionsThe dimensions that make it possible to measure quality and how each one reveals a different type of problem.
• Data quality toolsSoftware and features for cleansing, standardization, deduplication, and validation.
• Performance metrics and indicatorsError rate, completeness, duplication, and the PDCA cycle applied to continuous quality improvement.

• Definition of data cleansingWhat data cleansing is, what it is for, and how it directly affects the efficiency and reliability of the database.
• Data cleansing processesThe benefits of the process and how it improves results and organizational routines.
• Descriptive standardization of materialsThe stages of data cleansing and the role of each in building a solid foundation.
• Introduction to PDMHow standardization and data cleansing ensure consistency, eliminate duplication, and ease integration between systems.
• PDM structureRulebook, technical characteristics, classification tree, and workflow.
• PDM developmentThe six stages: cataloging, structuring, normalization, identification, standardization and systematization.
• Short and long descriptionsThe rules that ensure clarity and consistency in identifying materials in ERPs and internal processes.
• PDM processes: products, customers, and suppliersHow to build the descriptive standard for each domain, in practice.
• Short and long descriptions in practiceApplied exercise in creating descriptions that serve both operations and strategy.
• TaxonomyThe concept, its importance in data management, and the gains in consistency and accessibility.
• Units of measureHow the concept evolved and why the right choice is decisive for database consistency.
• Market classificationMarket taxonomies: how they help track trends, segment customers, and adjust commercial strategy.

• Definition of dataThe concept of data and its classifications: qualitative, quantitative, structured, and unstructured. The importance of data in decision-making.
• Structured and unstructured dataHow structured data is organized in tables and why text, documents, and media require advanced processing techniques.
• Master, reference, and transactional dataThe difference between the three and the main master data domains: people, business partners, materials and products.
• Business partner master dataEssential information, onboarding and approval, security and integration, and the role of MDM platforms and workflows.
• Material master dataCategorization of raw materials, products, and services; market classification tree, UNSPSC and NCM; differences between master data record maintenance in industry and in retail.
• Other master dataPhysical locations, digital assets, employees, ledger accounts, and cost centers, less visible but equally critical to operations.
• The importance of metadataData about data: creation date, owner, criticality. How metadata contextualizes, organizes, and enables governance.

• Definition of MDM softwareHow MDM software enables centralized, unified management of the organization's master data.
• Main components of an MDM systemThe modules of an MDM platform and how they work together to keep data consistent.
• Implementation modelsTechniques and approaches for deploying MDM software in real-world scenarios.
• Architectural componentsThe technical structure needed for a robust and sustainable implementation.
• Data integrationWhat a protocol is, what documentation is required, and how APIs connect MDM to satellite systems.
• Data modelingThe role of modeling in creating a logical structure that organizes and defines master data.
• Big Data and data visualizationHow data is processed in large volumes and which tools turn it into visual information.
• The future of MDM softwareEmerging trends, including blockchain, and how they are set to transform master data management.

• Introduction to data lineageHow to trace the complete path of data, from source to destination, and why this underpins reliability, governance, and LGPD.
• Technical characteristics of lineage softwareHow the tool maps, tracks, visualizes, and documents the information lifecycle and integrates with systems.

• Basic concepts of a data lakeHow it differs from the data warehouse, advantages, structures, tools and how to avoid the “data swamp”.
• Bronze, Silver, and Gold layersHow the layers organize data from raw ingestion to final analysis, and the role of MDM in this chain.

• Definition of riskThe origin of the risk concept and how mitigation techniques evolved into current practices.
• Types of risks associated with master dataIncomplete and duplicate data and leaks, and how these risks compromise decisions and operations.
• Risk analysisHow to identify and assess risks with a risk matrix and build effective action plans.
• Data privacySensitive data protection, GDPR and LGPD compliance, and access control for master data.
• Data auditThe five audit stages based on ISO 9001, 19011 and 27001: planning, execution, risks, report and follow-up.
• Data contractFormal agreements that define how information is exchanged between systems and organizations, with standards and quality guarantees.
• Compliance: definition and purposeLegal compliance, internal policies, and building an ethical culture in how data is handled.
• Local and global regulationsLGPD, GDPR, CCPA, and Privacy by Design: what each regulation requires in practice.
• ESG and its application in MDMHow environmental, social, and governance data are monitored and reported from a reliable foundation.
• Compliance and ethics awareness in MDMClear policies, training, auditing, and secure communication channels.
• Partner approval: pre-registration, registration, and approvalThe complete flow for onboarding and monitoring suppliers.
• Partner approval: performance evaluationHow to monitor and evaluate supplier performance throughout the relationship.
• Partner approval: second-party auditHow auditing keeps the supplier aligned with the company's standard and uncovers risks.
• Partner approval: performance certificationThe method for recognizing suppliers that meet or exceed the established standard.

• Advanced Excel formulasAutomate, clean and transform large master data sets with the formulas every specialist uses.
• Mathematical logic for specialistsThe operators that power the rules engine of any MDM platform.
• Logical operatorsAND, OR and NOT: how to create expressions evaluated as true or false to configure rules.
• Relational operatorsComparisons that become validation and filter rules, with examples for price, stock, and category.
• SQLHow SQL is applied in MDM and ETL processes: querying, joining and aggregating.
• Logical and relational operators with SQLHow to validate, filter, and manipulate master data in real queries.

• PythonIntroduction to the language: simplicity, versatility, and why it became a must-have tool in data.
• Variables and data typesHow to store numbers, text, and booleans, and manipulate data in master data management scenarios.
• Control structuresif, elif, and else to check values, validate fields, and categorize records.
• Data structuresLists, tuples, dictionaries, and sets to organize information efficiently.
• Loopsfor and while to review and validate large volumes automatically.
• FunctionsHow to organize code, reuse logic, and make your routine more efficient.
• String manipulationConcatenate, modify, and clean text: the foundation of programmatic data cleansing.
• List comprehensionsCreate and transform lists in one line, in a compact and readable way.
• Working with filesRead and write spreadsheets of products and suppliers in bulk.
• Useful libraries for dataThe libraries that automate processes and ensure database quality.

• Introduction and what SAP isWhat SAP is, why it dominates master data record maintenance in large companies, and where master data lives inside it.
• What is Standard, Z, or YThe difference between standard and custom objects, when to use each and how to identify them.
• How to download and install SAP GUIAll versions, the role of the GUI, and how to navigate it efficiently.
• Creating an entry to access customersHow to set up multiple entries in SAP GUI, the way consulting firms do.
• Navigating the menus (SPRO, transactions, menus)How to get around the system and customize the GUI for your day-to-day work.
• Configuring your SAP GUIDisplay and productivity settings that change the user experience.
• Environments and clients (DEV/QAS/PRD)What each environment is for and the care each one requires.
• Requests in SAPWhat requests are, why they matter, and the difference between Workbench and Customizing.

• Filling in the information in the fieldsField types (date, time, value) and the attention each one requires to maintain data integrity.
• Variants for programsSave and reuse predefined data sets to speed up repetitive tasks.
• Tables and programsHow to access, view, and manage these two essential elements of the system.
• Adding transactions to the main menuHow to organize access to the functions you use most.
• Running programs, functions, and transactionsDay-to-day operations, use of variants, and optimized navigation.

• Finding forms, invoices, and DANFEsSapscript and SmartForms, transactions SE71 and SMARTFORMS, layout testing, and field validation.
• Background and foreground executionThe difference between the two and how to configure jobs to automate time-consuming tasks.
• Printing informationOutput devices, tax document generation, and spool management through SP01.
• Menus for detailed programsThe “System” and “Help” menus: display, data export, and background execution.
• SM30: table updatesView and change data directly in tables, with the care needed to avoid impacting the system.
• SHDB: process mappingRecord and replay interactions to automate repetitive processes and bulk loads.

• SM04: list of logons on the serverMonitor who is logged in, which transactions are running, and how to end a session safely.
• SM50: active process monitoringIdentify processes that affect performance and act with caution on them.
• Debug ABAPAnalyze execution step by step, find errors and value deviations with breakpoints and watchpoints.
• Exporting data to Excel, TXT, and other formatsTake reports out of SAP for analysis, charts, and sharing.
• SE11, SE16, and SE16N: table dataFilter information, validate records, and investigate configuration or master data problems.

• Data migrationMapping, standardization, and validation; the role of ETL and the importance of a contingency plan.
• Migration tools within SAPLSMW, S/4HANA Migration Cockpit, and Data Services: features, advantages, and limits.
• Creating a migration project in SAPFrom the project in Fiori to the XML template and staging tables for loading into S/4HANA.

• Different types of artificial intelligenceWhich types of AI exist and where each one actually optimizes the master data management process.
• AI, blockchain, and the future of data managementEmerging trends in data platforms and why governance is a prerequisite for any trustworthy AI.
• Masterclasses and new monthly contentThe AI track gets new lessons every month, following what actually goes into production at companies.

• Definition of projectSpecific objective, temporality, uniqueness, and the importance of detailed planning.
• Definition of methodologyWhat a project management methodology is and how to choose the right one for each context.
• Types of project methodologiesPMBOK, PRINCE2, Agile (Scrum, Kanban), Waterfall, and Six Sigma: strengths and limits of each approach.
• Hybrid methodologyHow to combine traditional and agile practices to balance structure and flexibility.
• Definition of methodological artifactsPlans, schedules, and records that organize, control, and communicate progress.
• Initial phase artifactsProject charter, WBS, schedule, communication plan, RACI matrix, and risk management.
• Planning phase artifactsProject and communication plan: scope, objective, timeline, and responsibility.
• Execution phase artifactsField mapping, integration development, test plan and report.
• Monitoring phase artifactsPMO, status reports, and the handling of risks, deviations, and changes.
• Closing phase artifactsCustomer success, assisted operation after go-live, handover, and closure document.
• Importance of project management methodologyOrganization, cost and schedule control, delivery quality, and continuous improvement.
• The project management methodology in MDMHow to define scope, mitigate risk, and control cost in a master data project.
• Starting an MDM projectWelcome to the client, team introduction, checklist, and kickoff meeting.
• Planning an MDM projectInformation gathering, technical proposal, assumptions, and schedule approval with the client.
• Executing an MDM projectScenario mapping, integration, testing, cutover, and data migration.
• Monitoring an MDM projectProgress tracking, data quality, risk, change, and communication with stakeholders.
• Closing an MDM projectValidation of deliverables, team release, lessons learned, and satisfaction assessment.

• Definition of business caseHow to justify the investment, calculate ROI, and present the financial and strategic benefit.
• The strategic importance of the business caseHow to communicate the idea, align stakeholders, and sustain the decision. Includes a business case template to adapt.
• Identifying pain points with master dataError, duplication, and inconsistent description: the effect on operational and financial efficiency.
• Collecting metrics and defining the offendersPareto, data profiling, and Ishikawa diagram applied to the ACME S/A case.
• Defining corrective actionsSMART methodology, standardized processes, validation and deduplication through MDM, and team training.
• Presenting resultsHow to present tangible and intangible results and prepare alternative scenarios.

• Cloud or on-premiseCharacteristics, service models, and deployment options, with the financial and operational impact of each.
• Definition of cloud computingDirect comparison between cloud and on-premise: control, upfront cost, and flexibility.
• Types of software licensingProprietary, open source, freeware, shareware, and SaaS.
• Definition of SaaSAccessibility, automatic updates, cost reduction and market examples.
• Choosing MDM softwareIntegration, data quality, and scalability: the criteria that set a good solution apart.
• The role of the implementation consultantTechnical knowledge, analytical skills, and communication: the profile the market is hiring for.

• Technical and functional componentsCompany, administrator, and user portals; fields, templates, stages, businesses, and business groups.
• Login screenSingle sign-on, manual registration, self-registration, internationalization, and support channel.
• Sidebar menu, dashboard, and reportsNavigate the platform, interpret the dashboard, and create custom reports.
• Viewing a workflowCreate, edit, and manage instances; deactivate, annotate, and review the audit trail of each step.
• Header: notifications, chat, alerts, and chatbotCommunication between users, change history, pending-approval alerts, and language switching.
• Advanced searches and statisticsDetailed search, export for analysis, and charts configured by the administrator.
• Bulk creation and changesCreate and change many items at once, with accuracy and traceability.
• Monitoring bulk processingLoad status, error review, and direct correction in the spreadsheet for reprocessing.
• Administrator portal: users and departmentsCreate administrators, define responsibilities, limit records by department, and block access.
• Settings and auditVisual theme of the tenant, session expiration, and tracking of all portal changes.
• Business groups, businesses, and stagesOrganize flows into categories, define key fields, and configure validation and external interaction for each stage.
• Field types and validationsText, integer, decimal, date, list, and upload; character limits and regular expressions.
• Advanced multiple business rulesConditions and actions that make a field mandatory, validate information, or restrict access.
• Permissions, header, and listingAccess by user and department, informative header and field organization in the list.

• Self-awareness applied to career (DISC)How to identify your behavioral profile, understand the DISC test, and use the result to improve communication, relationships, and professional performance.
• Professional image and credibility in the corporate environmentHow to strengthen your image and convey trust, credibility, and authority in day-to-day interactions.
• Professional behavior, posture, and ethicsTechniques for communicating clearly, reducing misunderstandings, and building healthy, productive professional relationships.
• Emotional management in high-pressure contextsStrategies for handling pressure, controlling reactions, and deciding with more balance and emotional intelligence.

• Clear and objective communication (managing speaking anxiety)How to communicate clearly and objectively in the corporate environment and manage speaking anxiety to convey ideas with confidence and impact.
• Fundamentals of persuasion in the corporate environmentHow persuasion works in the corporate environment and how to structure your communication to build understanding and buy-in.
• Negotiation and influence techniques in the corporate environmentHow to negotiate strategically, seeking agreements that deliver results without creating unnecessary conflict.
• Structuring professional presentationsHow to organize a presentation clearly and strategically to drive understanding and action.

• Identifying conflicts and their originsHow to identify conflicts in the corporate environment and understand their origins, when departments have different goals, priorities, and ways of working.
• Mediation and negotiation techniquesActive listening, conversation structure, and solution focus to lead difficult conversations, promote alignment between departments, and reduce friction.
• Turning conflicts into opportunitiesHow to use conflict to identify failures, improve processes, strengthen communication, and drive growth in teams.

• A winning LinkedIn profile for data analystsHow to structure a profile that communicates value in a few seconds, following the case of a professional transitioning into data governance.
• How to find job openings and get foundHow the platform works, how to tune your profile, and how to use keywords and positioning to increase your visibility in the market.
• Strategic content creation on LinkedInWhich content works, how to structure strategic posts, and how to turn learning into professional opportunity.

• What is Data MeshThe core idea of Data Mesh, where it came from and which data architecture problem it seeks to solve.
• Why centralized architecture starts to failThe bottlenecks of a central data team and the signs that the centralized model has stopped scaling with the company's growth.
• Principle 1: data ownership by domainHow to transfer responsibility for data to the business areas that know it best, and what changes in the roles and boundaries of each domain.
• Principle 2: data as a productWhat it means to treat data as a product, with an owner, quality, documentation, and consumers who depend on it.
• Principle 3: self-service data platformThe platform that lets each domain's teams publish and consume data autonomously, without depending on a central team for every request.
• Principle 4: federated governanceHow to balance domain autonomy with common rules for quality, security, and interoperability, defined and enforced in a federated way.
• Anatomy of a data productThe components of a data product: the data, the metadata, the contract, the access interfaces, and the quality indicators.
• Data Mesh, Data Fabric, Lakehouse, and MDMThe differences and relationships between these approaches, and where MDM comes in to ensure master data consistency.
• How to implement Data Mesh in a companyWhere to start, how to choose the first domain, structure the teams, and grow adoption gradually.
• Data Mesh in practice: the ACME caseApplying the concepts in a complete case. Comes with a practical exercise, which is not a lesson.

• Historical context, the field's two winters and the shift to data.
• What artificial intelligence is
• Dartmouth and the birth of the field
• The expert systems of the 1980s
• The two AI winters
• The turning point of data and computing power
• From the lab to everyday business

• Explicit rules, predictive AI and generative AI, with a criterion for when to use each.
• Automation with explicit rules
• Predictive AI: forecasting from history
• Generative AI: creating new content
• When to use each one
• Weak AI, strong AI and what is just marketing
• Where each type shows up in data governance

• Training and testing, types of learning, overfitting and why bad data breaks any model.
• What it means to train a model
• Training, validation and test data
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Overfitting and underfitting
• Metrics: accuracy is not everything
• Why bad data breaks any model

• Neuron, layers, weights, the Transformer architecture and the attention mechanism, without the math.
• The artificial neuron
• Layers, weights and biases
• How the network adjusts its weights
• Deep learning: what changes with depth
• The Transformer architecture
• The attention mechanism
• Why the Transformer changed everything

• How they generate text, the context window, why they vary and why they hallucinate.
• What a language model is
• Pretraining, fine-tuning and alignment
• How the model chooses the next word
• Temperature and why the answer varies
• Context window
• What hallucination is and why it happens
• Open models and closed models
• Multimodality: text, image, audio and tables

• How meaning becomes numbers and how to find duplicates that the ERP search never finds.
• What a token is
• How text is split into tokens
• From token to number: vectorization
• What an embedding is
• Similarity: the distance between meanings
• Finding duplicates the ERP search cannot find
• Embeddings applied to material descriptions
• The limits of embeddings

• Vector databases, chunking, RAG and the direct dependence on trustworthy master data.
• Why the model needs your data
• What a vector database is
• Chunking: chunk size matters
• Indexing and retrieval
• What RAG is
• RAG in practice: from question to answer
• The direct dependence on trustworthy master data
• Why AI projects fail in the master records, not in the model

• Glossary, catalog, metadata, knowledge graphs and descriptive standardization.
• Syntax, semantics and meaning
• Business glossary
• Taxonomy and merchandise hierarchy
• Ontology: the relationship between concepts
• The metadata AI uses
• Data catalog
• Knowledge graphs
• Descriptive standardization of master records

• Bias, explainability, LGPD, confidentiality, intellectual property and regulation.
• Bias in data and in the model
• Explainability and the black-box problem
• LGPD and personal data inside the prompt
• Confidentiality and leaks through use
• Intellectual property of what AI generates
• Who is accountable for AI errors
• Internal AI use policy
• Regulation: the AI Act and the Brazilian landscape
• AI governance checklist

• Context, instruction structure, working with documents and the limits of basic use.
• Choosing the free tool
• The role of context
• The structure of a good instruction
• Asking for a format: table, list, text
• Working with documents
• Reviewing and iterating on the answer
• Checking what the AI answered
• The limits of basic use

• Projects, knowledge base, reusable skills, connectors and automation.
• Organizing work into projects
• Building a knowledge base
• Reusable instructions
• Skills and agents
• Connectors and system access
• Automating record maintenance routines
• Working with spreadsheets and volume
• When to stop and call a specialist

• Full diagnosis of a materials master with AI support, from exploration to action plan.
• ACME's material master
• Exploring the database with AI support
• Finding duplicates with embeddings
• Diagnosing quality and completeness
• Standardizing descriptions with AI support
• Building the action plan
• Presenting the results to leadership